In this section, we use the error analysis tool in [1]} to further explore how ADC help HPE models achieve better results. We mainly study four types of errors: 1) jitter: small error around the correct keypoint location; 2) missing: large localization error, the detected keypoint is not within the proximity of any body part; 3) inversion: confusion between semantically similar parts belonging to the same instance. The detection is in the proximity of the true keypoint location of the wrong body part; 4) swap: confusion between semantically similar parts of different instances. The detection is within the proximity of a body part belonging to a different person. We use SimpleBaseline-Res50 as the baseline model, and plot the error analysis results with and without ADC in Figure REF . As one can see, ADC can reduce the proportion of all four types of errors. Especially, the proportion of missing error is reduced by \(0.4\%\) . It suggests that ADC could help the model to be more robust and detect keypoints in more cases. This may be attributed to that ADC can adaptively adjust the dilation rates. The jitter error and inversion error directly indicate the accuracy of location and classification respectively. The proportions of these two errors are both reduced by \(0.2\%\) . It suggests that ADC can simultaneously benefit the location and classification of keypoints.
